45 citations · 126 across the 10 of their papers we have counts for
5 papers · 1 filter
Sample Efficient Model Evaluation
Emine Yilmaz, Peter Hayes, Raza Habib +2
Labelling data is a major practical bottleneck in training and testing classifiers. Given a collection of unlabelled data points, we address how to select which subset to label to…
Private Machine Learning via Randomised Response
David Barber
We introduce a general learning framework for private machine learning based on randomised response. Our assumption is that all actors are potentially adversarial and as such we tr…
Gaussian Mean Field Regularizes by Limiting Learned Information
Julius Kunze, Louis Kirsch, Hippolyt Ritter +1
Variational inference with a factorized Gaussian posterior estimate is a widely used approach for learning parameters and hidden variables. Empirically, a regularizing effect can b…
Practical Lossless Compression with Latent Variables using Bits Back Coding
James Townsend, Tom Bird, David Barber
Deep latent variable models have seen recent success in many data domains. Lossless compression is an application of these models which, despite having the potential to be highly u…
Modular Networks: Learning to Decompose Neural Computation
Louis Kirsch, Julius Kunze, David Barber
Scaling model capacity has been vital in the success of deep learning. For a typical network, necessary compute resources and training time grow dramatically with model size. Condi…